Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture.

Musculoskeletal Disorders (MSDs) stand as a prominent cause of injuries in modern agriculture. Scientific research has highlighted a causal link between MSDs and awkward working postures. Several methods for the evaluation of working postures, and related risks, have been developed such as the Rapid Upper Limb Assessment (RULA). Nevertheless, these methods are generally applied with manual measurements on pictures or videos. As a consequence, their applicability could be scarce, and their effectiveness could be limited. The use of wearable sensors to collect kinetic data could facilitate the use of these methods for risk assessment. Nevertheless, the existing system may not be usable in the agricultural and vine sectors because of its cost, robustness and versatility to the various anthropometric characteristics of workers. The aim of this study was to develop a technology capable of collecting accurate data about uncomfortable postures and repetitive movements typical of vine workers. Specific objectives of the project were the development of a low-cost, robust, and wearable device, which could measure data about wrist angles and workers' hand positions during possible viticultural operations. Furthermore, the project was meant to test its use to evaluate incongruous postures and repetitive movements of workers' hand positions during pruning operations in vineyard. The developed sensor had 3-axis accelerometers and a gyroscope, and it could monitor the positions of the hand-wrist-forearm musculoskeletal system when moving. When such a sensor was applied to the study of a real case, such as the pruning of a vines, it permitted the evaluation of a simulated sequence of pruning and the quantification of the levels of risk induced by this type of agricultural activity.

First Evaluation of Infrared Thermography as a Tool for the Monitoring of Udder Health Status in Farms of Dairy Cows.

The aim of the present study was to test infrared thermography (IRT), under field conditions, as a possible tool for the evaluation of cow udder health status. Thermographic images (n. 310) from different farms (n. 3) were collected and evaluated using a dedicated software application to calculate automatically and in a standardized way, thermographic indices of each udder. Results obtained have confirmed a significant relationship between udder surface skin temperature (USST) and classes of somatic cell count in collected milk samples. Sensitivity and specificity in the classification of udder health were: 78.6% and 77.9%, respectively, considering a level of somatic cell count (SCC) of 200,000 cells/mL as a threshold to classify a subclinical mastitis or 71.4% and 71.6%, respectively when a threshold of 400,000 cells/mL was adopted. Even though the sensitivity and specificity were lower than in other published papers dealing with non-automated analysis of IRT images, they were considered acceptable as a first field application of this new and developing technology. Future research will permit further improvements in the use of IRT, at farm level. Such improvements could be attained through further image processing and enhancement, and the application of indicators developed and tested in the present study with the purpose of developing a monitoring system for the automatic and early detection of mastitis in individual animals on commercial farms.

Development of a Machine Vision Method for the Monitoring of Laying Hens and Detection of Multiple Nest Occupations.

Free range systems can improve the welfare of laying hens. However, the access to environmental resources can be partially limited by social interactions, feeding of hens, and productivity, can be not stable and damaging behaviors, or negative events, can be observed more frequently than in conventional housing systems. In order to reach a real improvement of the hens' welfare the study of their laying performances and behaviors is necessary. With this purpose, many systems have been developed. However, most of them do not detect a multiple occupation of the nest negatively affecting the accuracy of data collected. To overcome this issue, a new "nest-usage-sensor" was developed and tested. It was based on the evaluation of thermografic images, as acquired by a thermo-camera, and the performing of patter recognitions on images acquired from the nest interior. The sensor was setup with a "Multiple Nest Occupation Threshold" of 796 colored pixels and a template of triangular shape and sizes of 43 × 33 pixels (high per base). It was tested through an experimental nesting system where 10 hens were reared for a month. Results showed that the evaluation of thermografic images could increase the detection performance of a multiple occupation of the nest and to apply an image pattern recognition technique could allow for counting the number of hens in the nest in case of a multiple occupation. As a consequence, the accuracy of data collected in studies on laying performances and behaviors of hens, reared in a free-range housing system, could result to be improved.

A Monitoring System for Laying Hens That Uses a Detection Sensor Based on Infrared Technology and Image Pattern Recognition.

In Italy, organic egg production farms use free-range housing systems with a big outdoor area and a flock of no more than 500 hens. With additional devices and/or farming procedures, the whole flock could be forced to stay in the outdoor area for a limited time of the day. As a consequence, ozone treatments of housing areas could be performed in order to reduce the levels of atmospheric ammonia and bacterial load without risks, due by its toxicity, both for hens and workers. However, an automatic monitoring system, and a sensor able to detect the presence of animals, would be necessary. For this purpose, a first sensor was developed but some limits, related to the time necessary to detect a hen, were observed. In this study, significant improvements, for this sensor, are proposed. They were reached by an image pattern recognition technique that was applied to thermografic images acquired from the housing system. An experimental group of seven laying hens was selected for the tests, carried out for three weeks. The first week was used to set-up the sensor. Different templates, to use for the pattern recognition, were studied and different floor temperature shifts were investigated. At the end of these evaluations, a template of elliptical shape, and sizes of 135 × 63 pixels, was chosen. Furthermore, a temperature shift of one degree was selected to calculate, for each image, a color background threshold to apply in the following field tests. Obtained results showed an improvement of the sensor detection accuracy that reached values of sensitivity and specificity of 95.1% and 98.7%. In addition, the range of time necessary to detect a hen, or classify a case, was reduced at two seconds. This result could allow the sensor to control a bigger area of the housing system. Thus, the resulting monitoring system could allow to perform the sanitary treatments without risks both for animals and humans.

First Results of a Detection Sensor for the Monitoring of Laying Hens Reared in a Commercial Organic Egg Production Farm Based on the Use of Infrared Technology.

The development of a monitoring system to identify the presence of laying hens, in a closed room of a free-range commercial organic egg production farm, was the aim of this study. This monitoring system was based on the infrared (IR) technology and had, as final target, a possible reduction of atmospheric ammonia levels and bacterial load. Tests were carried out for three weeks and involved 7 ISA (Institut de Sélection Animale) brown laying hens. The first 5 days was used to set up the detection sensor, while the other 15 days were used to evaluate the accuracy of the resulting monitoring system, in terms of sensitivity and specificity. The setup procedure included the evaluation of different color background (CB) thresholds, used to discriminate the information contents of the thermographic images. At the end of this procedure, a CB threshold equal to an increase of 3 °C from the floor temperature was chosen, and a cutoff level of 196 colored pixels was identified as the threshold to use to classify a positive case. The results of field tests showed that the developed monitoring system reached a fine detection accuracy (sensitivity = 97.9% and specificity = 94.9%) and the IR technology proved to be a possible solution for the development of a detection sensor necessary to reach the scope of this study.

Improved Fuzzy Logic System to Evaluate Milk Electrical Conductivity Signals from On-Line Sensors to Monitor Dairy Goat Mastitis.

The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0-60 Days In Milking (DIM); 61-120 DIM; 121-180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors.

Evaluation of the Fourier Frequency Spectrum Peaks of Milk Electrical Conductivity Signals as Indexes to Monitor the Dairy Goats' Health Status by On-Line Sensors.

The aim of this study is a further characterization of the electrical conductivity (EC) signal of goat milk, acquired on-line by EC sensors, to identify new indexes representative of the EC variations that can be observed during milking, when considering not healthy (NH) glands. Two foremilk gland samples from 42 Saanen goats, were collected for three consecutive weeks and for three different lactation stages (LS: 0-60 Days In Milking (DIM); 61-120 DIM; 121-180 DIM), for a total amount of 1512 samples. Bacteriological analyses and somatic cells counts (SCC) were used to define the health status of the glands. With negative bacteriological analyses and SCC < 1,000,000 cells/mL, glands were classified as healthy. When bacteriological analyses were positive or showed a SCC > 1,000,000 cells/mL, glands were classified as NH. For each milk EC signal, acquired on-line and for each gland considered, the Fourier frequency spectrum of the signal was calculated and three representative frequency peaks were identified. To evaluate data acquired a MIXED procedure was used considering the HS, LS and LS × HS as explanatory variables in the statistical model.Results showed that the studied frequency peaks had a significant relationship with the gland's health status. Results also explained how the milk EC signals' pattern change in case of NH glands. In fact, it is characterized by slower fluctuations (due to the lower frequencies of the peaks) and by an irregular trend (due to the higher amplitudes of all the main frequency peaks). Therefore, these frequency peaks could be used as new indexes to improve the performances of algorithms based on multivariate models which evaluate the health status of dairy goats through the use of gland milk EC sensors.

Preliminary evaluation of a nest usage sensor to detect double nest occupations of laying hens.

Conventional cage systems will be replaced by housing systems that allow hens to move freely. These systems may improve hens' welfare, but they lead to some disadvantages: disease, bone fractures, cannibalism, piling and lower egg production. New selection criteria for existing commercial strains should be identified considering individual data about laying performance and the behavior of hens. Many recording systems have been developed to collect these data. However, the management of double nest occupations remains critical for the correct egg-to-hen assignment. To limit such events, most systems adopt specific trap devices and additional mechanical components. Others, instead, only prevent these occurrences by narrowing the nest, without any detection and management. The aim of this study was to develop and test a nest usage "sensor", based on imaging analysis, that is able to automatically detect a double nest occupation. Results showed that the developed sensor correctly identified the double nest occupation occurrences. Therefore, the imaging analysis resulted in being a useful solution that could simplify the nest construction for this type of recording system, allowing the collection of more precise and accurate data, since double nest occupations would be managed and the normal laying behavior of hens would not be discouraged by the presence of the trap devices.

GC-MS and Sensory Analysis of Aqueous Extracts of Monovarietal American Hops, Produced Using the Synergy Pure™ Extraction Technique.

Aqueous extracts from four different American hops varieties (Mosaic, Chinook, Citra, and Centennial) were produced using the Synergy Pure technique, an innovative extraction distillation process developed by Synergy Flavours, a global specialist in the manufacturing of flavors, extracts and essences. This process is able to preserve and maximize the aromatic characteristics without increasing the bitterness of the final product. Therefore, the aim of this work is to identify the volatile and sensory characteristics of these extracts, with the additional aim to assess their suitability for brewing. GC-MS and sensory analyses were carried out on the four different aqueous extracts. The results highlighted the presence of 33 volatile compounds in a quantity that allowed us to identify the characteristics of the varieties under investigation in each extract. Sensory analysis was carried out using the expert sensory profiling technique. The results regarding the olfactory analysis of the extracts allowed us to define the aroma profiles of the four extracts, highlighting their strong correspondence with the characteristic of the varieties under investigation. Finally, the results showed that the aqueous extracts produced using the Synergy Pure extraction technique had a richer aroma profile, highlighting its higher aptitude in beer production.

Use of the electronic nose as a screening tool for the recognition of durum wheat naturally contaminated by deoxynivalenol: a preliminary approach.

Fungal contamination and the presence of related toxins is a widespread problem. Mycotoxin contamination has prompted many countries to establish appropriate tolerance levels. For instance, with the Commission Regulation (EC) N. 1881/2006, the European Commission fixed the limits for the main mycotoxins (and other contaminants) in food. Although valid analytical methods are being developed for regulatory purposes, a need exists for alternative screening methods that can detect mould and mycotoxin contamination of cereal grains with high sample throughput. In this study, a commercial electronic nose (EN) equipped with metal-oxide-semiconductor (MOS) sensors was used in combination with a trap and the thermal desorption technique, with the adoption of Tenax TA as an adsorbent material to discriminate between durum wheat whole-grain samples naturally contaminated with deoxynivalenol (DON) and non-contaminated samples. Each wheat sample was analysed with the EN at four different desorption temperatures (i.e., 180 °C, 200 °C, 220 °C, and 240 °C) and without a desorption pre-treatment. A 20-sample and a 122-sample dataset were processed by means of principal component analysis (PCA) and classified via classification and regression trees (CART). Results, validated with two different methods, showed that it was possible to classify wheat samples into three clusters based on the DON content proposed by the European legislation: (a) non-contaminated; (b) contaminated below the limit (DON < 1,750 µg/kg); (c) contaminated above the limit (DON > 1,750 µg/kg), with a classification error rate in prediction of 0% (for the 20-sample dataset) and 3.28% (for the 122-sample dataset).

The Use of Infrared Thermography for the Monitoring of Udder Teat Stress Caused by Milking Machines.

The aim of this study was to test infrared thermography (IRT) as a possible tool for scoring teat color changes after cluster removal; thus, indirectly, to classify the short-term stress of teats caused by milking machines. Thermographic images (n = 137) from three farms were collected and evaluated to calculate the average and maximum skin surface temperatures (SSTs) at the base, center, and tip of each teat (Tavg,B, Tavg,C, Tavg,T, Tmax,B, Tmax,C, and Tmax,T). Obtained results confirmed a significant relationship between the indicators Tavg, Tmax and the levels of teat color change (level one: pink-colored teat; level two: red-colored teat; level three: blue or purple-colored teat). Nevertheless, when a teat was considered to be stressed because its scoring fell in level 3 of the color-change scale used, sensitivity and specificity in the classification of the teat status ranged respectively between 45.6% and 54.3%, and 54.4% and 59.2%, for the indicators Tavg; and 56.5% and 60.9%, and 59.7% and 61.8%, for the indicators Tmax. When a teat was considered stressed because its scoring fell between the levels 2 and 3 of the scale adopted, sensitivity and specificity were between 49.0% and 55.8%, and 58.3% and 61.8%, for the indicators Tavg; and 55.8% and 59.9%, and 60.6% and 61.4%, for the indicators Tmax. As a consequence, the low values of sensitivity and specificity do not seem to justify the development of an ad hoc infrared device for the monitoring of udder teat stress. Nonetheless, this technology can be a viable solution for a preliminary evaluation of the mechanical stress of teats if a milking system would be equipped with an infrared sensor already in place for other purposes (e.g., the monitoring of udder health status).

The effect of anaerobic digestion and storage on indicator microorganisms in swine and dairy manure.

The aim of this experimental study was to evaluate the influence of anaerobic digestion and storage on indicator microorganisms in swine and dairy excreta. Samples were collected every 90 days for 15 months at eight farms, four pig, and four dairy farms, four of them having a biogas plant. Moreover, to evaluate storage effects on samples, 20 l of manure and slurry taken at each farm (digested manure only in farms with a biogas plant) were stored in a controlled climatic chamber at 18 °C, for 6 months. The bacterial load and the chemical-physical characteristics of excreta were evaluated at each sampling time, stored slurry, and manure were sampled and analyzed every 2 months. A high variability of the concentration of bacteria in the different excreta types was observed during the experiment, mainly depending on the type and time of treatment. No sample revealed either the presence of Escherichia coli O157:H7 or of Salmonella, usually linked to the temporary rearing of infected animals in facilities. Anaerobic digestion and storage affected in a significant way the reduction of indicator bacteria like lactobacilli, coliforms, and streptococci. Anaerobic digestion lowered coliforms in pig slurry (- 2.80 log, P < 0.05), streptococci in dairy manure (- 2.44 log, P < 0.001) and in pig slurry (- 1.43 log, P < 0.05), and lactobacilli in pig slurry (- 3.03 log, P < 0.05). Storage lowered coliforms and the other indicators counts, in particular in fresh wastes, while clostridia did not show a reduction in concentration.

Retarded germination of Nicotiana tabacum seeds following insertion of exogenous DNA mimics the seed persistent behavior.

Tobacco seeds show a coat-imposed dormancy in which the seed envelope tissues (testa and endosperm) impose a physical constraint on the radicle protrusion. The germination-limiting process is represented by the endosperm rupture which is induced by cell-wall weakening. Transgenic tobacco seeds, obtained by insertion of exogenous genes codifying for seed-based oral vaccines (F18 and VT2eB), showed retarded germination with respect to the wild type and modified the expression of endogenous proteins. Morphological and proteomic analyses of wild type and transgenic seeds revealed new insights into factors influencing seed germination. Our data showed that the interference of exogenous DNA influences the germination rather than the dormancy release, by modifying the maturation process. Dry seeds of F18 and VT2eB transgenic lines accumulated a higher amount of reserve and stress-related proteins with respect to the wild type. Moreover, the storage proteins accumulated in tobacco F18 and VT2eB dry seeds have structural properties that do not enable the early limited proteolysis observed in the wild type. Morphological observations by electron and light microscopy revealed a retarded mobilization of the storage material from protein and lipid bodies in transgenic seeds, thus impairing water imbibition and embryo elongation. In addition, both F18 and VT2eB dry seeds are more rounded than the wild type. Both the morphological and biochemical characteristics of transgenic seeds mimic the seed persistent profile, in which their roundness enables them to be buried in the soil, while the higher content of storage material enables the hypocotyl to elongate more and the cotyledons to emerge.

Echocardiographic measurements of cardiac dimensions in normal Standardbred racehorses.

OBJECTIVES: The aim of this study was to obtain echocardiographic measurements and establish reference ranges for 14 parameters in Standardbred racehorses in training. BACKGROUND: Several studies have been published about cardiac measurements in Thoroughbreds, Standardbreds, National Hunt horses, Warmbloods and ponies; however, not all parameters have been published for the Standardbred trotter in training. ANIMALS, MATERIALS AND METHODS: Thirty normal Standardbred racehorses in training were assessed by two-dimensional echocardiography (2-D) and M-mode echocardiography using standardized imaging planes. Mean values, standard deviations, 95% confidence interval for the means and 95% confidence interval for the cardiac parameters measured in the population were calculated. Furthermore, a general linear model was constructed using sex, age and body weight (bwt) of the horses as independent variables and the echocardiographic measurements as dependent variables. Multivariate linear regression analysis was performed with the level of significance at p<0.05 for all the null hypotheses. RESULTS: Reference ranges were established for 14 echocardiographic parameters in Standardbred racehorses. Weak linear relationships between echocardiographic measurements and body weight were observed for LVIDd, LVIDs, LVFWs, and AOD. Linear regressions on these parameters were used to calculate the 95% confidence intervals for the predicted values. CONCLUSIONS: The data collected in this study provide reference values for the evaluation of Standardbred racehorses in training. Body weight has a negligible affect on most echocardiographic parameters in this homogeneous population, but did mildly influence the results of left ventricular and aortic measurements.